The Interface
Deep television libraries often span hundreds or thousands of episodes.
Accessing the moments that drive digital typically depends on a handful of lifers who have seen everything, or months to years of manual research to build a searchable archive.
ClipMiner replaces institutional memory and long research cycles with a system your digital team can query in minutes.
We developed a focused stack and workflow that transforms deep content libraries into something your team can search in minutes.
We ingest individual broadcast caption files and related materials through automation, transforming fragmented archives into a private AI search engine built exclusively on your content.
Your team runs defined queries through a simple web interface. The system scans the full library and returns structured lists of moments with contextual information built for digital production.
Broadcast caption files, transcripts, episode metadata, and related production documents.
Captions are transformed into structured descriptions and indexed into a private, full-library AI search system.
Structured lists of moments with season, episode, act, and contextual dialogue ready for digital production.
Built for deep television libraries with hundreds or thousands of episodes and digital upside worth exploring or revisiting.
This includes game shows, reality series, court shows, talk shows, soaps, competition formats, and other long-running content formats.
If accessing usable moments depends on institutional memory or lengthy research cycles, this changes the equation.
ClipMiner is built by digital producers with billions of views across all major platforms on behalf of multiple global entertainment brands.
We have spent years working inside long-running television formats and understand how archives are actually mined, packaged, and monetized.
We built the tool we always needed and are now making it available to select partners.
ClipMiner is in early release. We are working with a limited number of partners to refine our product.
If this resonates, we should talk.